Assessing the suitability of DEMs derived from Sentinel-1 for landslide volume estimation

Each year, landslides cause numerous fatalities and significant infrastructure damages, leading to enormous economic loss. For example, in 2017, several massive landslides buried the only access road to the village of Vals in Tyrol, Austria, trapping approximately 130 persons for a few days. Likewise, in November 2019, heavy rainfall in Austria triggered catastrophic landslides in Upper Carinthia, East Tyrol, and southern Salzburg, resulting in people injured and dead, as well as destroyed infrastructure. Earth observation (EO) has proven its value to support emergency response and disaster risk management after landslide events by providing images of the affected areas, especially of hardly accessible mountain areas. The availability of free EO data such as the Copernicus (European Union's Earth Observation Programme) Sentinel missions opens a new era for identification, analysis, mapping, and characterisation of catastrophic and big landslides. The high temporal and spatial resolution of the Sentinel-1 A & B satellites opens a new era in the field of interferometric synthetic aperture radar (InSAR) analysis, and in particular its application in landslide analysis. Radar data are used for radar interferometry techniques, which allow the generation of digital elevation models (DEMs) for measuring the topography of a surface and its changes over time. However, there are only a few case studies that have applied SAR-based DEMs for geohazard analyses. Particularly, landslide volume estimation is a difficult task that requires information on the landslide area, landslide surface and sub-surface, landslide depth, and the geometry of the slope failure. The overall goal of SliDEM is to estimate landslide volumes based on DEMs derived from Sentinel-1 data. We will develop a workflow for the automated generation of DEMs from Sentinel-1 data and implement it using the open-source ESA Sentinel Application Platform (SNAP) for investigating landslide examples in Austria and Norway. The workflow includes i) image downloading and archiving, ii) image suitability analysis, iii) DEM generation, iv) DEM-co-registration and volume estimations. This project will significantly contribute to increasing our knowledge about the applicability of Sentinel-1 DEMs for landslide assessment.